Power distribution network fault risk suppression analysis method and system based on charging station power purchase guidance

Abstract

The invention provides a power distribution network fault risk suppression analysis method and system based on charging station power purchase guidance, and belongs to the field of power distribution network safe and economical operation. The method comprises the following steps: firstly, constructing a charging station generalized energy storage model based on Minkowski and theory, mapping the randomness of electric vehicle charging to the randomness of the schedulable potential of the charging station, and analyzing the probability characteristics of the scheduling potential of the charging station; secondly, under the uncertainty constraint of the scheduling potential of the charging station, introducing a power distribution network fault risk constraint condition into a traditional charging scheduling model, and constructing a power distribution network fault risk suppression scheduling model based on two-stage stochastic programming; furthermore, an analytic target decomposition algorithm is adopted, and a multi-charging-station collaborative power purchase guide model decoupling solving strategy considering power distribution network fault risk suppression is designed. The method can effectively reduce the electricity purchasing cost of the charging station while ensuring that the fault risk of the power distribution network is controlled within a safety level, and stimulates the enthusiasm of the charging station to participate in load regulation of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of safe and economical operation of power distribution networks, and in particular to a method and system for analyzing the risk suppression of power distribution network faults based on power purchase guidance from charging stations. Background Technology

[0002] The continuous integration of new power sources and loads, such as large-scale distributed photovoltaics, energy storage, and electric vehicles, has significantly increased the operational risks of distribution networks, making fault prevention and rapid response crucial challenges. Meanwhile, new loads like electric vehicles exhibit significant flexibility; flexible load guidance can improve power flow and voltage distribution in distribution networks, enhancing their fault prevention capabilities. However, due to the inherent randomness of electric vehicle charging, the scheduling potential of charging stations is difficult to quantify precisely, and the scheduling potential of charging stations is a key constraint on the participation of charging loads in grid operation regulation. This presents a significant challenge in incorporating the flexible adjustment capabilities of charging loads into the development of distribution network fault risk mitigation scheduling strategies. Therefore, effectively assessing the uncertainty of the upper and lower bounds of charging station scheduling potential and incorporating the flexible adjustment capabilities of electric vehicles and charging stations into distribution network fault risk management to develop charging station power purchase guidance strategies with fault risk mitigation capabilities has significant theoretical and practical value.

[0003] Domestic and international research on electricity purchase guidance for charging stations mainly focuses on network loss optimization and peak-valley difference reduction, lacking consideration for mitigating distribution network fault risks. In calculating the dispatchable potential of charging stations, the dispatchable potential is treated as a fixed value, failing to account for the uncertainty brought about by the randomness of electric vehicle charging. This leads to inaccurate definition of the upper and lower adjustment boundaries of charging stations, reducing the feasibility of electricity purchase guidance strategies. Regarding electricity purchase guidance strategies for charging stations, many focus on minimizing peak-valley differences or optimizing operational economy, neglecting the role of flexible charging resources in mitigating distribution network fault risks and failing to fully leverage the flexibility advantages of electric vehicle charging. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to propose a distribution network fault risk suppression analysis method and analysis system based on charging station power purchase guidance. By considering the impact of the randomness of electric vehicle charging on the randomness of the dispatchable potential of charging stations, and by constructing a segmented index function of distribution network fault risk, the distribution network fault risk is integrated into the traditional charging station power purchase guidance model, so that the distribution network charging scheduling has fault risk management capabilities, and effectively reduces the power purchase cost of charging stations while ensuring that the distribution network fault risk is controlled within a safe level.

[0005] In a first aspect, the present invention provides a method for analyzing the risk suppression of power distribution network faults based on electricity purchase guidance for charging stations, including: S1. Based on the energy and power coupling relationship of electric vehicle charging, a generalized energy storage model of the charging station is constructed, and then the mapping relationship between the randomness of electric vehicle charging and the parameters of the generalized energy storage model is established to obtain the probabilistic characteristics of the boundary parameters characterizing the dispatchable potential of the charging station. S2. Using the aforementioned probability characteristics as constraints, and combining the needs of distribution network operation optimization and charging station power purchase optimization, a risk constraint is constructed by introducing a segmented indicator function for distribution network line fault risk. Based on the master-slave game relationship between distribution network operators and charging stations, a distribution network fault risk suppression scheduling model guided by charging station power purchase is established. S3. Based on the aforementioned distribution network fault risk suppression scheduling model, and using the analytical objective decomposition algorithm, the original problem is decoupled into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, thereby obtaining the optimal power purchase guidance strategy under distribution network fault risk suppression.

[0006] Preferably, the generalized energy storage model of the charging station is constructed based on the energy and power coupling relationship of electric vehicle charging, and then a mapping relationship is established between the stochasticity of electric vehicle charging and the parameters of the generalized energy storage model. The probabilistic features of the boundary parameters characterizing the dispatchable potential of the charging station include: Based on the energy and power coupling constraints of individual electric vehicle charging, and using Minkowski theory, the constraints of all electric vehicles in the station are aggregated to construct a generalized energy storage model of the entire charging station, thereby obtaining a deterministic boundary of the dispatchable potential. Based on the randomness of electric vehicle arrival and departure times and the corresponding statistical probability characteristics, a mapping relationship between charging stations and the randomness of charging status is established, and the Edgeworth series approximation method is used to calculate the probability distribution characteristics of key boundary parameters in the generalized energy storage model.

[0007] Preferably, the step of using the probability characteristics as constraints, combining the distribution network operation optimization needs and the charging station power purchase optimization needs, introducing a distribution network line fault risk segmentation indicator function to construct risk constraints, and establishing a distribution network fault risk suppression scheduling model based on charging station power purchase guidance based on the master-slave game relationship between the distribution network operator and the charging station includes: Based on the correlation characteristics between different line load rate levels and fault risk, a segmented indicator function for distribution network fault risk is constructed, and the line fault risk constraints of the distribution network are constructed based on the degree of importance that distribution network operators attach to the operation risk of the distribution network. Based on the master-slave game relationship between distribution network operators and charging stations, and with the goal of minimizing distribution network operating costs, a distribution network scheduling optimization model is constructed, taking into account power balance constraints, line fault risk constraints, and line transmission capacity constraints. At the same time, with the goal of minimizing the expected cost of electricity purchase by charging stations, an electricity purchase optimization model for charging stations is constructed, taking into account the scheduling potential constraints of the generalized energy storage model and probability characteristics, electric vehicle charging demand constraints, and battery physical constraints. By combining the aforementioned distribution network scheduling optimization model and charging station power purchase optimization model, a distribution network fault risk suppression scheduling model based on charging station power purchase guidance is constructed.

[0008] Preferably, according to the distribution network fault risk suppression scheduling model, based on the analytical objective decomposition algorithm, the original problem is decoupled into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, and the optimal power purchase guidance strategy under distribution network fault risk suppression is obtained, including: The objective function and constraints of the distribution network fault risk suppression scheduling model are decomposed step by step using the analytical objective decomposition algorithm, and the original problem is decomposed into the upper-level main problem of the distribution network operator and the lower-level sub-problems corresponding to each charging station. In the objective functions of the upper-level main problem and the lower-level subproblem, penalty terms for the deviation of the electricity purchase / sale plan are introduced respectively, and a convergence criterion is set. By iteratively solving and updating the penalty coefficient, the optimization plans of the master and slave sides tend to be consistent in information interaction until the convergence criterion is met, and the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

[0009] Preferably, in the objective functions of the upper-level main problem and the lower-level sub-problems, penalty terms for deviations in electricity purchase / sale plans are introduced respectively, and convergence criteria are set. By iteratively solving and updating the penalty coefficients, the optimization plans of the master and slave sides tend to be consistent in information exchange until the convergence criteria are met, thus obtaining the optimal electricity purchase guidance strategy under game equilibrium. The deviation between the expected day-ahead electricity purchase / sale plan of the charging station by the distribution network operator and the actual plan formulated by the charging station is used as a penalty term, and the penalty term is introduced into the objective function of the upper-level main problem and the lower-level sub-problem respectively. Initialize the penalty coefficient, and optimize and solve the problems at different levels independently; After each iteration, the deviation of the interaction variable is compared with the preset convergence criterion. If convergence is not achieved, the penalty coefficient is increased geometrically, and the interaction information is updated based on the current optimization result before proceeding to the next iteration. When the deviation of the interaction variable satisfies the convergence criterion, the iteration terminates, and the game equilibrium solution is obtained, that is, the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

[0010] Secondly, the present invention also provides a power distribution network fault risk suppression analysis system based on charging station power purchase guidance, applied to the aforementioned method, including: The dispatchable potential analysis unit is used to construct a generalized energy storage model of the charging station based on the energy and power coupling relationship of electric vehicle charging, and then establish a mapping relationship between the randomness of electric vehicle charging and the parameters of the generalized energy storage model to obtain the probabilistic characteristics of the boundary parameters characterizing the dispatchable potential of the charging station. The model building unit is used to take the probability features as constraints, combine the power distribution network operation optimization needs and the charging station power purchase optimization needs, introduce the power distribution network line fault risk segmentation indicator function to construct risk constraints, and establish a power distribution network fault risk suppression scheduling model based on charging station power purchase guidance based on the master-slave game relationship between the power distribution network operator and the charging station. The model solving unit is used to decouple the original problem into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body, based on the analytical objective decomposition algorithm according to the distribution network fault risk suppression scheduling model. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, so as to obtain the optimal power purchase guidance strategy under the distribution network fault risk suppression.

[0011] Preferably, the schedulable potential analysis unit is specifically used for: Based on the energy and power coupling constraints of individual electric vehicle charging, and using Minkowski theory, the constraints of all electric vehicles in the station are aggregated to construct a generalized energy storage model of the entire charging station, thereby obtaining a deterministic boundary of the dispatchable potential. Based on the randomness of electric vehicle arrival and departure times and the corresponding statistical probability characteristics, a mapping relationship between charging stations and the randomness of charging status is established, and the Edgeworth series approximation method is used to calculate the probability distribution characteristics of key boundary parameters in the generalized energy storage model.

[0012] Preferably, the model building unit is specifically used for: Based on the correlation characteristics between different line load rate levels and fault risk, a segmented indicator function for distribution network fault risk is constructed, and the line fault risk constraints of the distribution network are constructed based on the degree of importance that distribution network operators attach to the operation risk of the distribution network. Based on the master-slave game relationship between distribution network operators and charging stations, and with the goal of minimizing distribution network operating costs, a distribution network scheduling optimization model is constructed, taking into account power balance constraints, line fault risk constraints, and line transmission capacity constraints. At the same time, with the goal of minimizing the expected cost of electricity purchase by charging stations, an electricity purchase optimization model for charging stations is constructed, taking into account the scheduling potential constraints of the generalized energy storage model and probability characteristics, electric vehicle charging demand constraints, and battery physical constraints. By combining the aforementioned distribution network scheduling optimization model and charging station power purchase optimization model, a distribution network fault risk suppression scheduling model based on charging station power purchase guidance is constructed.

[0013] Preferably, the model solving unit is specifically used for: The objective function and constraints of the distribution network fault risk suppression scheduling model are decomposed step by step using the analytical objective decomposition algorithm, and the original problem is decomposed into the upper-level main problem of the distribution network operator and the lower-level sub-problems corresponding to each charging station. In the objective functions of the upper-level main problem and the lower-level subproblem, penalty terms for the deviation of the electricity purchase / sale plan are introduced respectively, and a convergence criterion is set. By iteratively solving and updating the penalty coefficient, the optimization plans of the master and slave sides tend to be consistent in information interaction until the convergence criterion is met, and the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

[0014] Preferably, the iterative optimization module is used for: The deviation between the expected day-ahead electricity purchase / sale plan of the charging station by the distribution network operator and the actual plan formulated by the charging station is used as a penalty term, and the penalty term is introduced into the objective function of the upper-level main problem and the lower-level sub-problem respectively. Initialize the penalty coefficient, and optimize and solve the problems at different levels independently; After each iteration, the deviation of the interaction variable is compared with the preset convergence criterion. If convergence is not achieved, the penalty coefficient is increased geometrically, and the interaction information is updated based on the current optimization result before proceeding to the next iteration. When the deviation of the interaction variable satisfies the convergence criterion, the iteration terminates, and the game equilibrium solution is obtained, that is, the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention presents a distribution network fault risk suppression analysis method based on charging station power purchase guidance. By constructing a generalized energy storage model and mapping the relationship between charging randomness and model parameters, the probabilistic characteristics of the dispatchable potential boundary parameters of charging stations are obtained. This solves the problem of inaccurate boundary definition caused by charging randomness and provides reliable constraints for scheduling strategies. By introducing a segmented indicator function for distribution network line fault risk, fault risk suppression is incorporated into power purchase guidance optimization, leveraging the fault defense value of flexible adjustment of electric vehicles. Furthermore, a scheduling model is established based on a master-slave game relationship. An analytical objective decomposition algorithm is used to decouple and iterate the upper and lower level problems, taking into account both distribution network operation optimization and charging station power purchase demand. The resulting optimal power purchase guidance strategy can effectively suppress distribution network fault risks and tap the potential for flexible adjustment of charging load, thereby improving the safety and economy of distribution network operation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0017] Figure 1 A flowchart of a distribution network fault risk suppression analysis method based on charging station power purchase guidance provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a power distribution network fault risk suppression analysis system based on charging station power purchase guidance, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] like Figure 1 As shown in the figure, a method for analyzing the risk suppression of power distribution network faults based on charging station power purchase guidance is provided in an embodiment of the present invention, including: Step S1: Based on the energy and power coupling relationship of electric vehicle charging, construct a generalized energy storage model for the charging station, and then establish a mapping relationship between the randomness of electric vehicle charging and the parameters of the generalized energy storage model to obtain the probabilistic characteristics of the boundary parameters characterizing the schedulable potential of the charging station. To assess the dispatchable potential of charging stations, this invention treats charging stations as generalized energy storage. Based on the energy-power coupling relationship of electric vehicles, the dispatchable potential characteristics of a single electric vehicle are derived. Based on Minkowski sum theory, the adjustable potential boundary of a single electric vehicle is aggregated into the dispatchable potential boundary of the charging station. Furthermore, by introducing the randomness of the electric vehicle's charging state, the boundary parameters of the charging station's dispatchable potential are calculated.

[0020] The dispatchable potential of a charging station stems from the dispatchable capacity of all electric vehicles within the station. The dispatchable capacity of a single electric vehicle must meet the energy and power constraints during its charging process. These energy and power constraints during the electric vehicle charging process are shown in formulas (1)-(4): (1) (2) (3) (4) in, Indicates the first n electric vehicles at all times t The state of charge (SOC); Indicates the first n electric vehicles at all times t The random charging state variable (1 when charging, 0 otherwise); The sampling interval; and These represent charging and discharging efficiencies, respectively. and They represent the first n electric vehicles at all times t The charging and discharging power; This is the discharge compensation coefficient; and They represent the first n The lower and upper limits of battery energy for electric vehicles; and These represent the maximum charging and discharging power, respectively.

[0021] The energy-power coupling constraints of a single electric vehicle during the charging process constitute a convex polyhedron composed of equality and inequality constraints. Therefore, through Minkowski theory, the energy-power coupling constraints of all electric vehicles within the station can be aggregated into the energy-power coupling constraints of the charging station, forming the schedulable potential space of the charging station, as shown in Equation (5). Furthermore, the relationship between the energy and power parameters of the charging station and the energy and power parameters of the electric vehicles in Equation (5) is defined as shown in Equation (6): (5) (6) The energy and power parameters represented by uppercase letters in Formula (5) have similar meanings to the parameters of a single electric vehicle in Formula (6), but the parameters in Formula (5) represent the energy and power parameters of the charging station. Indicates the first i electric vehicle clusters at time t Energy change due to state of charge transition. In formula (6), Indicates the number of charging stations i A collection of electric vehicles and They represent the first n State of Charge (SOC) of an electric vehicle when it arrives at and leaves a charging station.

[0022] As mentioned above, the dispatchable potential of a charging station can be determined by a set of parameters. The randomness of the charging station's schedulable potential parameters is characterized by each parameter being related to the charging state of the electric vehicles within the station. Since the charging state of an individual electric vehicle is determined by its arrival and departure times, the randomness of the charging station's schedulable potential parameters can be characterized by deriving the mapping relationship between the randomness of electric vehicle arrival and departure times and the randomness of charging state.

[0023] By using probability fitting based on historical statistical data, the probability density function (PDF) and cumulative distribution function (CDF) of the arrival and departure times of different types of electric vehicles within a charging station can be obtained. Let... and Let represent the random variables representing the arrival and departure of the electric vehicle from the charging station, respectively. and The corresponding PDF is given. The probability that an electric vehicle is in a charging state can be expressed by formula (7): (7) In formula (7), The condition that must be met for the arrival and departure times of an electric vehicle while it is charging is expressed, and its calculation logic is shown in formula (8): (8) The dispatchable potential of a charging station is influenced by the joint distribution of the random variables of the charging states of all electric vehicles within the station. Assuming that the random variables of the charging states of electric vehicles are independent and identically distributed, in engineering applications, the PDF of the dispatchable potential parameters of the charging station can be fitted using the Edgeworth series shown in formula (9): (9) in, This represents a jointly distributed random variable composed of multiple independent and identically distributed variables; Indicates the number of independent and identically distributed random variables; Represents random variables The first moment; express Standard deviation; The CDF represents the standard normal distribution; Indicates dependence on random variables First-order Edgeworth series polynomial with third-order moments; PDF representing the standard normal distribution.

[0024] The first-order polynomial of an Edgeworth series polynomial is defined as: (10) in, Represents the second moment of a random variable; It represents the third moment of a random variable.

[0025] Next, all moments of the schedulable potential parameters of the electric vehicle cluster are calculated. Since these random variables are linearly related to the state of charge and energy change of individual electric vehicles, the moments of the random variables of the schedulable potential of the electric vehicle cluster can be directly derived by calculating the moments of the state of charge and energy change of individual electric vehicles.

[0026] The first moment is calculated based on the PDF of the individual electric vehicle's state of charge and energy change, as shown in equations (11)-(12): (11) (12) in, Represents random variables The first moment; Represents random variables The first moment.

[0027] The second moment of the random variables of charging state and energy change of a single electric vehicle is calculated as shown in formulas (13)-(14): (13) (14) in, Represents random variables The second moment; Represents random variables The second moment.

[0028] Similarly, the third moments of the random variables of the charging state and energy change of a single electric vehicle are calculated as shown in formulas (15)-(16): (15) (16) in, Represents random variables The third moment; Represents random variables The third moment.

[0029] Therefore, the first to third moments of the random variables of charging state and energy change of individual electric vehicles were calculated. From this, all moments of the random variable of the schedulable potential of the electric vehicle cluster can be derived. Due to the linear relationship, these moments can be directly calculated using formula (17): (17) in, ( ) represents the order of the moment.

[0030] Substituting all the moments of the schedulable potential parameters of the electric vehicle cluster calculated by equation (17) into equations (9)-(10), the CDF of the schedulable potential parameters of the electric vehicle cluster or charging station can be calculated. Further differentiation yields an approximate PDF, thus obtaining the probabilistic characteristics of each parameter of the schedulable potential of the charging station.

[0031] By constructing a generalized energy storage model for charging stations, a mapping relationship between the randomness of electric vehicle charging and model parameters is established, the probabilistic characteristics of boundary parameters are obtained, and the potential uncertainty brought about by charging randomness is accurately quantified, making the boundary of dispatchable potential more reasonable and providing reliable constraints for electricity purchase guidance strategies.

[0032] S2. Using the aforementioned probability characteristics as constraints, and combining the needs of distribution network operation optimization and charging station power purchase optimization, a risk constraint is constructed by introducing a segmented indicator function for distribution network line fault risk. Based on the master-slave game relationship between distribution network operators and charging stations, a distribution network fault risk suppression scheduling model guided by charging station power purchase is established.

[0033] In the process of guiding electricity purchases at charging stations, since the distribution network operator acts as the price setter and the charging station acts as the price taker, the guidance of electricity purchases at charging stations is mathematically expressed as a Stackelberg game model, constrained by the upper and lower bounds of the charging station's dispatchable potential. Considering the uncertainty of the dispatchable potential constraint and the distribution network fault risk constraint, a two-stage stochastic programming model combined with a Stackelberg game model is introduced to optimize the electricity purchase guidance strategy for multiple charging stations and ensure that the distribution network fault risk remains within an acceptable range.

[0034] As the top-level leader, the distribution network operator's goal is to minimize operating costs. To achieve this goal, the distribution network operator can guide each charging station to respond to load regulation demands by adjusting the marginal electricity price at each node through phased purchases of electricity from the upper-level grid. Objective function As shown in equation (18): (18) in, Indicates the time of distribution network operator t The m The power purchased from the upper-level power grid in each price segment; Indicates that the distribution network operator in the m The price of electricity purchased from the upper-level power grid in each price segment; Indicates charging station j In the recent market moment t The purchase and sale prices of electricity; This represents the set of price segments from which distribution network operators purchase electricity from the upper-level power grid; This represents the set of all charging stations.

[0035] The pricing guidance optimization model for distribution network operators needs to satisfy multiple constraints. The node power balance constraint is shown in equation (19): (19) in, and They represent charging stations j In the recent market moment t The power purchased and sold; Indicates time t Middle Branch Road The transmission power, of which Indicates from node i To the node j Directed branch, node i For nodes j The parent node, the node j For nodes i child nodes; Represents a node j The set of child nodes; Represents a node j At any moment t Normal load; It represents the set of all nodes in the distribution network.

[0036] To constrain the fault risk of the distribution network, a line fault risk function is introduced. The line fault risk depends on the severity of the line load, which is related to the line load factor. The line load factor and the line load severity are defined as follows: and The calculation method is shown in equations (20) and (21): (20) (twenty one) In equations (20) and (21), and Representing time respectively t line l The transmission power and maximum transmission power; q This represents the maximum permissible line load rate, which is usually set to 1. and This corresponds to the line load rate parameter; and This is the corresponding line load rate severity parameter, which can be set according to the importance of the line load rate severity.

[0037] Therefore, considering the risk of power distribution network failures, the power purchase guidance for charging stations must meet the following safety constraints, as shown in formulas (22)-(25): (twenty two) (twenty three) (twenty four) (25) In equations (22)-(25), Indicates time t line l The risk of failure; For the line l The set threshold values ​​for fault risk indicators will affect the formulation of risk scheduling plans. It can be set to the line risk level under traditional safety constraints and economic dispatch, and Adjustments can be made based on the importance of operational risks; and Representing time respectively t line l The probability of failure and the severity of load rate; , , , The correlation coefficient between the severity of line load rate and the load rate is calculated using equation (21). Furthermore, at time... t The fault risk of the entire distribution network is represented as .

[0038] The system power balance constraint is shown in equation (26): (26) in, Represents the set of child nodes of the balanced node; Indicates time t Branch power of the balancing node.

[0039] The branch power transmission capacity constraints and generator price segmented capacity constraints are shown in formulas (27)-(28): (27) (28) in, Indicates time t branch road Maximum transmission power; L Represents the set of branches; Indicates time t No. m The maximum power of the generator in each price segment.

[0040] Since charging stations need to consider the uncertainty of electric vehicle charging when formulating their electricity purchase plans, their electricity purchase decision-making process can be divided into two stages: day-ahead and intraday. The decision variables in the day-ahead stage include the charging station's electricity purchase and sale plan, which needs to be reported to the distribution network operator to optimize the total load of the distribution network. The decision variables in the intraday stage consist of real-time purchase and sale strategies under typical probability scenarios, which are used to correct the errors caused by the uncertainty of the day-ahead plan. Therefore, the charging station's day-ahead electricity purchase strategy needs to consider its impact on the intraday stage. By integrating the two stages, the objective function of the charging station's charging and discharging optimization model is to minimize the expected total electricity purchase cost, as shown in equation (29): (29) in, and These represent the charging stations during the day's phase. k In each scene, at any moment t The power volume purchased or sold from power distribution network operators; and Indicates the distribution network at time t The set daily purchase and sale price of electricity.

[0041] The charging and discharging optimization model for charging stations needs to consider the upper and lower bounds of power purchase and sale constraints and dispatchable potential constraints. The power purchase and sale constraints are defined as follows: (30) (31) (32) in, and These represent the charging stations at the [number]th [location]. k In a typical probability scenario, at time... t The generalized energy storage charging and discharging power; the first k The probability of each scenario is obtained through Latin hypercube sampling.

[0042] The constraints of generalized energy storage are as follows. Equation (33) represents the state of charge constraint of the electric vehicle battery. Equation (34) defines the charging power constraint of the electric vehicle, Equation (35) defines the discharging power constraint of the electric vehicle, Equation (36) is the periodic condition that ensures the battery state of charge is equal to the initial state of charge at the final moment, and Equation (37) is the mutual exclusion constraint of charging and discharging states, ensuring that the electric vehicle cannot charge and discharge simultaneously. However, since the charging and discharging efficiency of the electric vehicle is not 100%, simultaneous charging and discharging will not occur, and this constraint is redundant, so it can be omitted.

[0043] (33) (34) (35) (36) (37) Equations (18) to (37) construct a distribution network fault risk suppression scheduling model based on charging station power purchase guidance.

[0044] By introducing a segmented indication function for the fault risk of distribution network lines, the fault risk mitigation requirement is integrated into the power purchase guidance model, making up for the shortcomings of ignoring fault risk, effectively addressing the safety operation risks brought about by the access of new power sources and loads, and ensuring that the fault risk of the distribution network is controlled at a safe level.

[0045] S3. Based on the aforementioned distribution network fault risk suppression scheduling model, and using the analytical objective decomposition algorithm, the original problem is decoupled into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, thereby obtaining the optimal power purchase guidance strategy under distribution network fault risk suppression.

[0046] Given the large scale of electric vehicles and charging stations, the distribution network fault risk mitigation scheduling model based on charging station-guided electricity purchases will face challenges related to high-dimensional data. To improve the solution efficiency and feasibility of the model, an analytical objective decomposition algorithm is introduced to decouple the solution of the distribution network fault risk mitigation model based on multi-charging station electricity purchases.

[0047] The core idea of ​​the analytical objective decomposition algorithm is to decompose the original model into hierarchical subproblems through multi-level decoupling. The objective function and constraints of the original model are decomposed hierarchically, and each subproblem establishes an independent optimization model for solution. Subproblems pass solution response information to adjacent subproblems, and adjacent levels solve their own models based on the passed information and continue to pass information upwards. This process iteratively updates in a bottom-up manner. After receiving response information, the top-level master problem provides feedback and updates the sub-objectives of the lower-level subproblems. During the solution process, problems at different levels are solved independently and iteratively optimized until the inter-level coupling variables satisfy the convergence criterion.

[0048] The master-slave game model consisting of distribution network operators and charging stations is decoupled based on the principle of analytical objective decomposition algorithm. Distribution network operators are assigned to the master problem layer, and charging stations are assigned to the sub-problem layer.

[0049] Power distribution network operators and charging stations have different daily power purchase capacities. and electricity sales capacity The interaction and mutual coupling between them make independent solutions difficult. This coupling leads to the following consistency constraints, corresponding to a balance of interests: (38) in, and These represent the day-ahead electricity purchase and sales plans of the charging stations as expected by the distribution network operator. To satisfy this consistency constraint, a penalty term is introduced into the objective function of the upper-level optimization model to minimize the daily electricity purchase and sales power set by the charging station. and With expected power and The deviation between them. The objective function of the upper-level problem introduces an absolute value penalty term based on equation (18), as shown in equation (39): (39) in, Indicates the first l The penalty coefficient for each iteration cycle. Similarly, a similar penalty term is introduced into the objective function of the lower-level optimization model, as shown in equation (40): (40) At the start of each iteration, the initial value of the penalty coefficient is typically set to a small constant, having little impact on the final objective function value. Therefore, the upper and lower level problems initially optimize their own objectives to maximize their individual interests. With each iteration... As the deviation increases, its weight in the overall objective function is amplified. To minimize the overall objective, the upper and lower level problems adjust their strategies to reduce deviation and avoid excessively high penalty values. This mechanism ensures the satisfaction of system coupling constraints, causing the expected power purchase and sale by the distribution network operator and charging stations to gradually converge, ultimately achieving consistency constraints. The convergence criterion is constructed based on the deviation between the interaction variable and the target optimization variable, as shown in equation (41): (41) in, Let represent the convergence criterion, which is a small constant. To gradually approach the equilibrium point and achieve convergence of the consistency constraint through iteration cycles, the penalty coefficient needs to be gradually increased in each iteration, as shown in equation (42): (42) in, This represents the acceleration factor, which is usually set to a value between 1 and 3 to improve the convergence speed. After decoupling, the distribution network operator optimizes the load curve in the upper-level problem, while the charging station optimizes its day-ahead power purchase and sale plan and charging behavior in the lower-level problem. The upper and lower-level models continuously exchange power information to guide the decision-making of each entity, ultimately achieving a balance of interests.

[0050] This invention presents a distribution network fault risk suppression analysis method based on charging station power purchase guidance. By constructing a generalized energy storage model and mapping the relationship between charging randomness and model parameters, the probabilistic characteristics of the dispatchable potential boundary parameters of charging stations are obtained. This solves the problem of inaccurate boundary definition caused by charging randomness and provides reliable constraints for scheduling strategies. By introducing a segmented indicator function for distribution network line fault risk, fault risk suppression is incorporated into power purchase guidance optimization, leveraging the fault defense value of flexible adjustment of electric vehicles. Furthermore, a scheduling model is established based on a master-slave game relationship. An analytical objective decomposition algorithm is used to decouple and iterate the upper and lower level problems, taking into account both distribution network operation optimization and charging station power purchase demand. The resulting optimal power purchase guidance strategy can effectively suppress distribution network fault risks and tap the potential for flexible adjustment of charging load, thereby improving the safety and economy of distribution network operation.

[0051] like Figure 2 As shown, a power distribution network fault risk mitigation analysis system based on charging station power purchase guidance includes: a dispatchable potential analysis unit, a model building unit, and a model solving unit.

[0052] The schedulable potential analysis unit analyzes the probabilistic characteristics of the schedulable potential of charging stations for: The dispatchable potential of a charging station stems from the dispatchable capacity of all electric vehicles within the station. The dispatchable capacity of a single electric vehicle must meet the energy and power constraints during its charging process. These energy and power constraints during the electric vehicle charging process are shown in formulas (43)-(46): (43) (44) (45) (46) in, Indicates the first n electric vehicles at all times t The state of charge (SOC); Indicates the first n electric vehicles at all times t The random charging state variable (1 when charging, 0 otherwise); Sampling interval (15 minutes); and These represent charging and discharging efficiencies, respectively. and They represent the first n electric vehicles at all times t The charging and discharging power; This is the discharge compensation coefficient; and They represent the first n The lower and upper limits of battery energy for electric vehicles; and These represent the maximum charging and discharging power, respectively.

[0053] The energy-power coupling constraints of a single electric vehicle during the charging process constitute a convex polyhedron composed of equality and inequality constraints. Therefore, through Minkowski theory, the energy-power coupling constraints of all electric vehicles in the station can be aggregated into the energy-power coupling constraints of the charging station, forming the schedulable potential space of the charging station, as shown in formula (47). Furthermore, the relationship between the energy and power parameters of the charging station and the energy and power parameters of the electric vehicle in formula (47) is defined as shown in formula (48): (47) (48) The energy and power parameters represented by uppercase letters in formula (47) have similar meanings to the parameters of a single electric vehicle in formula (48), but the parameters in formula (47) represent the energy and power parameters of the charging station. Indicates the first ielectric vehicle clusters at time t Energy change caused by state of charge transition. In formula (48), Indicates the number of charging stations i A collection of electric vehicles and They represent the first n State of Charge (SOC) of an electric vehicle when it arrives at and leaves a charging station.

[0054] As mentioned above, the dispatchable potential of a charging station can be determined by a set of parameters. The randomness of the charging station's schedulable potential parameters is characterized by each parameter being related to the charging state of the electric vehicles within the station. Since the charging state of an individual electric vehicle is determined by its arrival and departure times, the randomness of the charging station's schedulable potential parameters can be characterized by deriving the mapping relationship between the randomness of electric vehicle arrival and departure times and the randomness of charging state.

[0055] By using probability fitting based on historical statistical data, the probability density function (PDF) and cumulative distribution function (CDF) of the arrival and departure times of different types of electric vehicles within a charging station can be obtained. Let... and Let represent the random variables representing the arrival and departure of the electric vehicle from the charging station, respectively. and The corresponding PDF is given. The probability that an electric vehicle is in a charging state can be expressed as equation (49): (49) In equation (49), The conditions that must be met for the arrival and departure times of an electric vehicle while it is charging are expressed, and the calculation logic is shown in formula (50): (50) The dispatchable potential of a charging station is influenced by the joint distribution of the random variables of the charging states of all electric vehicles within the station. Assuming that the random variables of the charging states of electric vehicles are independent and identically distributed, in engineering applications, the PDF of the dispatchable potential parameters of the charging station can be fitted using the Edgeworth series shown in formula (51): (51) in, This represents a jointly distributed random variable composed of multiple independent and identically distributed variables; Indicates the number of independent and identically distributed random variables; Represents random variables The first moment; express Standard deviation; The CDF represents the standard normal distribution; Indicates dependence on random variables First-order Edgeworth series polynomial with third-order moments; PDF representing the standard normal distribution.

[0056] The first-order polynomial of an Edgeworth series polynomial is defined as: (52) in, Represents the second moment of a random variable; It represents the third moment of a random variable.

[0057] Next, all moments of the schedulable potential parameters of the electric vehicle cluster are calculated. The first moment is calculated based on the PDF of the individual electric vehicle's state of charge and energy change, as shown in equations (53)-(54): (53) (54) in, Represents random variables The first moment; Represents random variables The first moment.

[0058] The second moment of the random variables of charging state and energy change of a single electric vehicle is calculated as shown in formulas (55)-(56): (55) (56) in, Represents random variables The second moment; Represents random variables The second moment.

[0059] Similarly, the third moment of the random variables of the charging state and energy change of a single electric vehicle is calculated as shown in formulas (57)-(58): (57) (58) in, Represents random variables The third moment; Represents random variables The third moment.

[0060] This allows us to derive all the moments of the schedulable potential random variables for electric vehicle clusters.

[0061] (59) in, ( ) represents the order of the moment.

[0062] Substituting all the moments of the schedulable potential parameters of the electric vehicle cluster calculated by equation (59) into equations (51)-(52), the CDF of the schedulable potential parameters of the electric vehicle cluster or charging station can be calculated. Further differentiation yields an approximate PDF, thus obtaining the probabilistic characteristics of each parameter of the schedulable potential of the charging station.

[0063] The model building unit constructs a distribution network fault risk suppression scheduling model based on charging station power purchase guidance, which is used for: As the top-level leader, the distribution network operator's goal is to minimize operating costs. To achieve this goal, the distribution network operator can guide each charging station to respond to load regulation demands by adjusting the marginal electricity price at each node through phased purchases of electricity from the upper-level grid. Objective function As shown in equation (60): (60) in, Indicates the time of distribution network operator t The m The power purchased from the upper-level power grid in each price segment; Indicates that the distribution network operator in the m The price of electricity purchased from the upper-level power grid in each price segment; Indicates charging station j In the recent market moment t The purchase and sale prices of electricity; This represents the set of price segments from which distribution network operators purchase electricity from the upper-level power grid; This represents the set of all charging stations.

[0064] The pricing guidance optimization model for distribution network operators needs to satisfy multiple constraints. The node power balance constraint is shown in equation (61): (61) in, and They represent charging stations j In the recent market moment t The power purchased and sold; Indicates time t Middle Branch Road The transmission power, of which Indicates from node i To the node j Directed branch, node i For nodes j The parent node, the node j For nodes i child nodes; Represents a node j The set of child nodes; Represents a node j At any moment t Normal load; It represents the set of all nodes in the distribution network.

[0065] To constrain the fault risk of the distribution network, a line fault risk function is introduced. The line fault risk depends on the severity of the line load, which is related to the line load factor. The line load factor and the line load severity are defined as follows: and The calculation method is shown in equations (62) and (63): (62) (63) In equations (62) and (63), and Representing time respectively t line l The transmission power and maximum transmission power; q This represents the maximum permissible line load rate, which is usually set to 1. and This corresponds to the line load rate parameter; and This is the corresponding line load rate severity parameter, which can be set according to the importance of the line load rate severity.

[0066] Therefore, considering the risk of power distribution network failures, the power purchase guidance for charging stations must meet the following safety constraints, as shown in formulas (64)-(67): (64) (65) (66) (67) In equations (66)-(67), Indicates time t line l The risk of failure; For the line l The set threshold values ​​for fault risk indicators will affect the formulation of risk scheduling plans. It can be set to the line risk level under traditional safety constraints and economic dispatch, and Adjustments can be made based on the importance of operational risks; and Representing time respectively t linel The probability of failure and the severity of load rate; , , , The correlation coefficient between the severity of line load rate and the load rate is calculated using equation (63). Furthermore, at time... t The fault risk of the entire distribution network is represented as .

[0067] The system power balance constraint is shown in equation (68): (68) in, Represents the set of child nodes of the balanced node; Indicates time t Branch power of the balancing node.

[0068] The branch power transmission capacity constraints and generator price segmented capacity constraints are shown in formulas (69)-(70): (69) (70) in, Indicates time t branch road Maximum transmission power; L Represents the set of branches; Indicates time t No. m The maximum power of the generator in each price segment.

[0069] The day-ahead electricity purchase strategy of charging stations needs to consider its impact on the intraday phase. By integrating the two phases, the objective function of the charging station charging and discharging optimization model is to minimize the expected total electricity purchase cost, as shown in equation (71): (71) in, and These represent the charging stations during the day's phase. k In each scene, at any moment t The power volume purchased or sold from power distribution network operators; and Indicates the distribution network at time t The set daily purchase and sale price of electricity.

[0070] The charging and discharging optimization model for charging stations needs to consider the upper and lower bounds of power purchase and sale constraints and dispatchable potential constraints. The power purchase and sale constraints are defined as follows: (72) (73) (74) in, and These represent the charging stations at the [number]th [location]. k In a typical probability scenario, at time... t The generalized energy storage charging and discharging power; the first k The probability of each scenario is obtained through Latin hypercube sampling.

[0071] The constraints of generalized energy storage are as follows. Equation (75) represents the state of charge constraint of the electric vehicle battery. Equation (76) defines the charging power constraint of the electric vehicle, Equation (77) defines the discharging power constraint of the electric vehicle, Equation (78) is the periodic condition that ensures the battery state of charge is equal to the initial state of charge at the final moment, and Equation (79) is the mutual exclusion constraint of charging and discharging states, ensuring that the electric vehicle cannot charge and discharge simultaneously. However, since the charging and discharging efficiency of the electric vehicle is not 100%, simultaneous charging and discharging will not occur, and this constraint is redundant, so it can be omitted.

[0072] (75) (76) (77) (78) (79) The model solving unit implements the accurate iterative solution of the distribution network fault risk suppression scheduling model guided by charging station power purchase, and is used for: The master-slave game model consisting of distribution network operators and charging stations is decoupled based on the principle of analytical objective decomposition algorithm. Distribution network operators are assigned to the master problem layer, and charging stations are assigned to the sub-problem layer.

[0073] Power distribution network operators and charging stations have different daily power purchase capacities. and electricity sales capacity The interaction and mutual coupling between them make independent solutions difficult. This coupling leads to the following consistency constraints, corresponding to a balance of interests: (80) in, and These represent the day-ahead electricity purchase and sales plans of the charging stations as expected by the distribution network operator. The model solving unit also includes an iterative optimization module, which is used to accelerate convergence during the model solving iterative process, and is used for: To satisfy this consistency constraint, a penalty term is introduced into the objective function of the upper-level optimization model to minimize the daily electricity purchase and sales power set by the charging station. and With expected power and The deviation between them. The objective function of the upper-level problem introduces an absolute value penalty term based on equation (60), as shown in equation (81): (81) in, Indicates the first l The penalty coefficient for each iteration cycle. Similarly, a similar penalty term is introduced into the objective function of the lower-level optimization model, as shown in equation (82): (82) At the start of each iteration, the initial value of the penalty coefficient is typically set to a small constant, having little impact on the final objective function value. Therefore, the upper and lower level problems initially optimize their own objectives to maximize their individual interests. With each iteration... As the deviation increases, its weight in the overall objective function is amplified. To minimize the overall objective, the upper and lower level problems adjust their strategies to reduce deviation and avoid excessively high penalty values. This mechanism ensures the satisfaction of system coupling constraints, causing the expected power purchase and sale by the distribution network operator and charging stations to gradually converge, ultimately achieving consistency constraints. The convergence criterion is constructed based on the deviation between the interaction variable and the target optimization variable, as shown in equation (83): (83) in, Let represent the convergence criterion, which is a small constant. To gradually approach the equilibrium point and achieve convergence of the consistency constraint through iteration cycles, the penalty coefficient needs to be gradually increased in each iteration, as shown in equation (84): (84) in, This represents the acceleration factor, which is usually set to a value between 1 and 3 to improve the convergence speed. After decoupling, the distribution network operator optimizes the load curve in the upper-level problem, while the charging station optimizes its day-ahead power purchase and sale plan and charging behavior in the lower-level problem. The upper and lower-level models continuously exchange power information to guide the decision-making of each entity, ultimately achieving a balance of interests.

[0074] The functional explanation of each unit / module in this embodiment is the same as that of a distribution network fault risk suppression analysis method based on charging station power purchase guidance, and the technical effects are the same, so it will not be repeated here.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for analyzing and mitigating distribution network fault risks based on charging station electricity purchase guidance, characterized in that, include: S1. Based on the energy and power coupling relationship of electric vehicle charging, a generalized energy storage model of the charging station is constructed, and then the mapping relationship between the randomness of electric vehicle charging and the parameters of the generalized energy storage model is established to obtain the probabilistic characteristics of the boundary parameters characterizing the dispatchable potential of the charging station. S2. Using the aforementioned probability characteristics as constraints, and combining the needs of distribution network operation optimization and charging station power purchase optimization, a risk constraint is constructed by introducing a segmented indicator function for distribution network line fault risk. Based on the master-slave game relationship between distribution network operators and charging stations, a distribution network fault risk suppression scheduling model guided by charging station power purchase is established. S3. Based on the aforementioned distribution network fault risk suppression scheduling model, and using the analytical objective decomposition algorithm, the original problem is decoupled into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, thereby obtaining the optimal power purchase guidance strategy under distribution network fault risk suppression.

2. The method for analyzing and mitigating distribution network fault risks based on charging station power purchase guidance as described in claim 1, characterized in that, The generalized energy storage model for charging stations is constructed based on the energy and power coupling relationship of electric vehicle charging. Then, a mapping relationship is established between the stochasticity of electric vehicle charging and the parameters of the generalized energy storage model. The probabilistic characteristics of the boundary parameters characterizing the dispatchable potential of the charging station include: Based on the energy and power coupling constraints of individual electric vehicle charging, and using Minkowski theory, the constraints of all electric vehicles in the station are aggregated to construct a generalized energy storage model of the entire charging station, thereby obtaining a deterministic boundary of the dispatchable potential. Based on the randomness of electric vehicle arrival and departure times and the corresponding statistical probability characteristics, a mapping relationship between charging stations and the randomness of charging status is established, and the Edgeworth series approximation method is used to calculate the probability distribution characteristics of key boundary parameters in the generalized energy storage model.

3. The method for analyzing and mitigating distribution network fault risks based on charging station power purchase guidance as described in claim 1, characterized in that, The process involves using the probabilistic characteristics as constraints, combining the needs of distribution network operation optimization and charging station power purchase optimization, introducing a segmented indicator function for distribution network line fault risk to construct risk constraints, and establishing a distribution network fault risk suppression scheduling model based on charging station power purchase guidance, based on the master-slave game relationship between distribution network operators and charging stations. Based on the correlation characteristics between different line load rate levels and fault risk, a segmented indicator function for distribution network fault risk is constructed, and the line fault risk constraints of the distribution network are constructed based on the degree of importance that distribution network operators attach to the operation risk of the distribution network. Based on the master-slave game relationship between distribution network operators and charging stations, and with the goal of minimizing distribution network operating costs, a distribution network scheduling optimization model is constructed, taking into account power balance constraints, line fault risk constraints, and line transmission capacity constraints. At the same time, with the goal of minimizing the expected cost of electricity purchase by charging stations, an electricity purchase optimization model for charging stations is constructed, taking into account the scheduling potential constraints of the generalized energy storage model and probability characteristics, electric vehicle charging demand constraints, and battery physical constraints. By combining the aforementioned distribution network scheduling optimization model and charging station power purchase optimization model, a distribution network fault risk suppression scheduling model based on charging station power purchase guidance is constructed.

4. The method for analyzing and mitigating distribution network fault risks based on charging station power purchase guidance as described in claim 1, characterized in that, The step, based on the distribution network fault risk mitigation scheduling model and an analytical objective decomposition algorithm, decouples the original problem into an upper-level optimization problem centered on the distribution network operator and a lower-level optimization problem centered on each charging station. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, thus obtaining the optimal electricity purchase guidance strategy under distribution network fault risk mitigation. The objective function and constraints of the distribution network fault risk suppression scheduling model are decomposed step by step using the analytical objective decomposition algorithm, and the original problem is decomposed into the upper-level main problem of the distribution network operator and the lower-level sub-problems corresponding to each charging station. In the objective functions of the upper-level main problem and the lower-level subproblem, penalty terms for the deviation of the electricity purchase / sale plan are introduced respectively, and a convergence criterion is set. By iteratively solving and updating the penalty coefficient, the optimization plans of the master and slave sides tend to be consistent in information interaction until the convergence criterion is met, and the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

5. The method for analyzing and mitigating distribution network fault risks based on charging station power purchase guidance as described in claim 4, characterized in that, The objective functions of the upper-level main problem and the lower-level sub-problems respectively introduce penalty terms for deviations in electricity purchase / sales plans, and set convergence criteria. By iteratively solving and updating the penalty coefficients, the optimization plans of the master and slave sides tend to be consistent in information exchange until the convergence criteria are met, thus obtaining the optimal electricity purchase guidance strategy under game equilibrium. The deviation between the expected day-ahead electricity purchase / sale plan of the charging station by the distribution network operator and the actual plan formulated by the charging station is used as a penalty term, and the penalty term is introduced into the objective function of the upper-level main problem and the lower-level sub-problem respectively. Initialize the penalty coefficient, and optimize and solve the problems at different levels independently; After each iteration, the deviation of the interaction variable is compared with the preset convergence criterion. If convergence is not achieved, the penalty coefficient is increased geometrically, and the interaction information is updated based on the current optimization result before proceeding to the next iteration. When the deviation of the interaction variable satisfies the convergence criterion, the iteration terminates, and the game equilibrium solution is obtained, that is, the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

6. A power distribution network fault risk mitigation analysis system based on charging station power purchase guidance, characterized in that, The application of the method as described in any one of claims 1-5 includes: The dispatchable potential analysis unit is used to construct a generalized energy storage model of the charging station based on the energy and power coupling relationship of electric vehicle charging, and then establish a mapping relationship between the randomness of electric vehicle charging and the parameters of the generalized energy storage model to obtain the probabilistic characteristics of the boundary parameters characterizing the dispatchable potential of the charging station. The model building unit is used to take the probability features as constraints, combine the power distribution network operation optimization needs and the charging station power purchase optimization needs, introduce the power distribution network line fault risk segmentation indicator function to construct risk constraints, and establish a power distribution network fault risk suppression scheduling model based on charging station power purchase guidance based on the master-slave game relationship between the power distribution network operator and the charging station. The model solving unit is used to decouple the original problem into an upper-level optimization problem with the distribution network operator as the main body and a lower-level optimization problem with each charging station as the main body, based on the analytical objective decomposition algorithm according to the distribution network fault risk suppression scheduling model. By introducing a penalty term and setting convergence criteria and penalty coefficient update rules, optimization information is iteratively exchanged between the upper and lower-level problems until a game equilibrium is reached, so as to obtain the optimal power purchase guidance strategy under the distribution network fault risk suppression.

7. The power distribution network fault risk mitigation analysis system based on charging station power purchase guidance according to claim 6, characterized in that, The schedulable potential analysis unit is specifically used for: Based on the energy and power coupling constraints of individual electric vehicle charging, and using Minkowski theory, the constraints of all electric vehicles in the station are aggregated to construct a generalized energy storage model of the entire charging station, thereby obtaining a deterministic boundary of the dispatchable potential. Based on the randomness of electric vehicle arrival and departure times and the corresponding statistical probability characteristics, a mapping relationship between charging stations and the randomness of charging status is established, and the Edgeworth series approximation method is used to calculate the probability distribution characteristics of key boundary parameters in the generalized energy storage model.

8. The power distribution network fault risk suppression analysis system based on charging station power purchase guidance according to claim 6, characterized in that, The model building unit is specifically used for: Based on the correlation characteristics between different line load rate levels and fault risk, a segmented indicator function for distribution network fault risk is constructed, and the line fault risk constraints of the distribution network are constructed based on the degree of importance that distribution network operators attach to the operation risk of the distribution network. Based on the master-slave game relationship between distribution network operators and charging stations, and with the goal of minimizing distribution network operating costs, a distribution network scheduling optimization model is constructed, taking into account power balance constraints, line fault risk constraints, and line transmission capacity constraints. At the same time, with the goal of minimizing the expected cost of electricity purchase by charging stations, an electricity purchase optimization model for charging stations is constructed, taking into account the scheduling potential constraints of the generalized energy storage model and probability characteristics, electric vehicle charging demand constraints, and battery physical constraints. By combining the aforementioned distribution network scheduling optimization model and charging station power purchase optimization model, a distribution network fault risk suppression scheduling model based on charging station power purchase guidance is constructed.

9. A power distribution network fault risk mitigation analysis system based on charging station power purchase guidance according to claim 6, characterized in that, The model solving unit is specifically used for: The objective function and constraints of the distribution network fault risk suppression scheduling model are decomposed step by step using the analytical objective decomposition algorithm, and the original problem is decomposed into the upper-level main problem of the distribution network operator and the lower-level sub-problems corresponding to each charging station. In the objective functions of the upper-level main problem and the lower-level subproblem, penalty terms for the deviation of the electricity purchase / sale plan are introduced respectively, and a convergence criterion is set. By iteratively solving and updating the penalty coefficient, the optimization plans of the master and slave sides tend to be consistent in information interaction until the convergence criterion is met, and the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

10. The power distribution network fault risk suppression analysis system based on charging station power purchase guidance according to claim 9, wherein the model solving unit includes an iterative optimization module, the iterative optimization module being used for: The deviation between the expected day-ahead electricity purchase / sale plan of the charging station by the distribution network operator and the actual plan formulated by the charging station is used as a penalty term, and the penalty term is introduced into the objective function of the upper-level main problem and the lower-level sub-problem respectively. Initialize the penalty coefficient, and optimize and solve the problems at different levels independently; After each iteration, the deviation of the interaction variable is compared with the preset convergence criterion. If convergence is not achieved, the penalty coefficient is increased geometrically, and the interaction information is updated based on the current optimization result before proceeding to the next iteration. When the deviation of the interaction variable satisfies the convergence criterion, the iteration terminates, and the game equilibrium solution is obtained, that is, the optimal electricity purchase guidance strategy under the game equilibrium is obtained.

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